Guest Editors
Prof. Yu-Hsien Lin
Email: vyhlin@mail.ncku.edu.tw
Affiliation: Dept. System and Naval Mechatronic Engineering, National Cheng Kung University, Tainan, 701, Taiwan
Homepage:
Research Interests: computational fluid dynamics, deep reinforcement learning, unmanned vehicle control
Summary
Reinforcement Learning (RL) has emerged as a pivotal domain within artificial intelligence, driving advancements in decision-making, optimization, and autonomy. This special issue, titled "Reinforcement Learning: Algorithms, Challenges, and Applications," aims to bring together cutting-edge research and practical insights that address the evolving landscape of RL. We invite contributions that explore novel algorithms, tackle persistent challenges such as scalability and safety, and showcase transformative applications across industries including robotics, healthcare, finance, and gaming. This issue seeks to provide a comprehensive overview of RL's current state while fostering discussions on its future directions. Both theoretical advancements and empirical studies are welcomed, with an emphasis on interdisciplinary approaches and real-world implementations.
Topics:
• Novel reinforcement learning algorithms
• Scalable RL techniques for high-dimensional environments
• RL safety, robustness, and ethical considerations
• Applications of RL in robotics, healthcare, and autonomous systems
• Multi-agent reinforcement learning and cooperation dynamics
• Model-based vs. model-free RL methods
• RL benchmarks, datasets, and evaluation metrics
Keywords
Reinforcement Learningm, Algorithms, Scalability, Applications, Safety, Multi-Agent Systems, Optimization
Published Papers
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